xMap - Junior Data Scientist

Data Scientist
💰 Negotiable
📍 United States
Twine Jobs Twine
Based in Manchester, United Kingdom
Last online 2 months ago

Data Scientist is needed in United States.

This is a remote position.

  • Data Extraction & Crawling: Assist in the automation of collecting geospatial data from APIs, databases, and web sources using tools like Selenium, Scrapy, or custom scripts.
  • Data Cleaning: Learn how to ensure the quality and consistency of geographic datasets by addressing missing or inconsistent data with guidance from senior engineers.
  • Data Preparation & Aggregation: Support in organizing and structuring geospatial data for use in xMap’s mapping and AI-driven analysis tools.
  • Data Manipulation: Work with geographic datasets to sort, filter, and transform them under the guidance of experienced team members.
  • Data Quality Assurance: Participate in implementing checks to maintain data integrity and geospatial accuracy.
  • Data Pipeline Development: Assist in building and maintaining automated data pipelines to support xMap’s platform.
  • Performance Optimization: Learn how to optimize data structures and queries for speed and efficiency in geospatial applications.
  • Automation & Process Improvement: Collaborate on automating repetitive tasks to streamline processes.
  • Collaboration & Communication: Work closely with xMap’s engineering team, GIS experts, and project managers, gaining experience in delivering high-quality data solutions.
  • Troubleshooting & Support: Assist in diagnosing and resolving issues in data pipelines, ensuring smooth operation of geospatial applications.

Requirements

  • Knowledge of Data Manipulation Languages: Strong skills in Python and SQL, with a willingness to learn more about handling geospatial data.
  • Knowledge in Web Scraping: Exposure to tools like Selenium, BeautifulSoup, or Scrapy is a plus, but not required.
  • Geospatial Data Awareness: Eagerness to learn about geographic data formats such as GeoJSON and shapefiles.
  • Data Preparation Tools: Basic experience with pandas, NumPy, or other data manipulation libraries is helpful but can be developed on the job.
  • Data Quality Management: An interest in learning about data validation and ensuring accuracy in mapping datasets.
  • Pipeline Design: Willingness to develop skills in tools like Apache Airflow, Luigi, or similar, with a focus on data pipelines.
  • Problem-Solving Mindset: Eagerness to grow in troubleshooting skills for resolving issues in data pipelines.
  • Collaboration Skills: Ability to communicate effectively and work well with cross-functional teams.
Posted 2 months ago

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